Posts tagged predictive analytics
Trump’s Brand Positioning One Year In

State of The POTUS - Text Analytics Reveals the Reasons Behind Trumps Approval Ratings

Over the past few weeks we’ve heard political pundits on all major news networks chime in on how Trump is doing one year after taking office. Part of the discussion is around what he has and hasn’t done, but an even bigger part continues to be about how he is perceived, both domestically and abroad, and some very grim opinion/approval polling is available. Many polls  have Trump as the President with the lowest approval ratings in history.

Sadly, Political Polling, including approval ratings, tells us absolutely nothing about the underlying causes for the ratings. Therefore, I thought I’d share our findings in this area. Utilizing our text analytics software, OdinText, we have been tracking not just sentiment related to Trump, but more importantly, the positioning of 40+ topics/themes that are important predictors of the sentiment.. In the brief analysis below, I will not have time to go into each of the attributes we have identified as important drivers, I will focus on a few of the areas which have seen the most change for Trump during the past year.

How has the opinion of Trump changed in the minds of the American people?

By looking at Trump’s positioning just before he took office (with all the campaign positioning fresh in the minds of the people), and comparing it to half a year into his office, and again now a full year into office, we can get a good idea about the impact various issues have on approval ratings and even more importantly, positioning.

Let’s start by looking back to just before he was elected. OdinText’s Ai uncovered the 15 most significant changes in perception since just before Trump won the election and now. Trump has fallen on 11 of these attributes and increased on 4.

Trump Pre Election Positioning VS One Year In

Tromp-Then-VS-Now.jpg

If we compare Trump just before the election VS Trump today, we several key differences. More recently four themes have become more important in terms of describing what Trump stands for in the minds of Americans when we include everyone (both those who like and dislike him). These newer positions are “Less Regulation”, “Healthcare Reform”, “Money/Greed”, and “Dishonesty”. Interestingly, text analytics reveals that one of the important issues seems to be changing, Trumps supporters are now more likely to be use the term “Healthcare Reform” rather than the previous “Repeal Obamacare”.

Other than the repeal of Obamacare issue, prior to the election, in the minds of Americans Trump was more likely to be associated with “Gun Rights”, “Honesty”, “Trade Deals”, “Change”, Supporting “Pro Life”, pro and con “Immigration” related issues including “The Wall”, and finally his slogan “MAGA” (Make America Great Again).

The decrease in relevance of many of these issues has to do with pre-election positioning, both by the Trump/Republican Party, as well as the Democrats Counter Positioning of him. After the election seemingly, some of these like ‘Gun Control’ have become less important for various reasons.

Five Months from Record Low

If we look at changes between this past Summer and now, there has been significantly less movement in terms of his positioning in American minds. He has seen a slight but significant bump in overall positive emotional sentiment/Joy, and the MAGA positioning as well as on Taxes, the economy, and The Wall, while also seeing a decrease in “Anger” and “Hate/Racism” which peaked this summer.

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His lowest point so far in the minds of Americans was during the August 12th, 2017 White Nationalist Rally in Charlottesville. Trump’s positioning as a Hate Monger was almost as high as the weekend before the election, while simultaneously positive emotional sentiment and ‘MAGA’ among his supporters was at an all time low.

Since the August low Trump does appear to have rebounded some, and while one year into office many believe the one thing Trump now stands for is himself, greed and money are a lesser evil in America than hate and racism.

It seems that one year into office, at least for now, the economy and tax cuts are giving Trump a bit of a bump back to pre-election levels in the minds of many Americans.

I’m not sure what the future holds in this case, but I hope you like me found some of the underlying reasons for his approval ratings of interest. These are after all more important than simple ratings, because these reasons are levers that can be changed to affect the final outcomes and positioning of any brand, including that of a POTUS.

@TomHCAnderson

[Note: Curious if OdinText’s new Ai can help you understand what drives your brands ratings? Request more info or early access to our brand new release here]

A New Trend in Qualitative Research

Almost Half of Market Researchers are doing Market Research Wrong! - My Interview with the QRCA (And a Quiet New Trend - Science Based Qualitative).

Two years ago I shared some research on research about how market researchers view Quantitative and Qualitative research. I stated that almost half of researchers don’t understand what good data is. Some ‘Quallies’ tend to rely and work almost exclusively with comment data from extremely small samples (about 25% of market researchers surveyed), conversely there is a large group of ‘Quant Jockey’s’ who while working with larger more representative sample sizes, purposefully avoid any unstructured data such as open ended comments because they don’t want to deal with coding and analyzing it or don’t believe in it’s accuracy and ability to add to the research objectives. In my opinion both researcher groups have it totally wrong, and are doing a tremendous disservice to their companies and clients.  Today, I’ll be focusing on just the first group above, those who tend to rely primarily on qualitative research for decisions.

Note that today’s blog post is related to a recent interview, which I was asked to take part in by the QRCA’s (Qualitative Research Consultant’s Association) Views Magazine. When they contacted me I told them that in most cases (with some exceptions), Text Analytics really isn’t a good fit for Qualitative Researchers, and asked if they were sure they wanted to include someone with that opinion in their magazine? I was told that yes, they were ok with sharing different viewpoints.

I’ll share a link to the full interview in the online version of the magazine at the bottom of this post. But before that, a few thoughts to explain my issues with qualitative data and how it’s often applied as well as some of my recent experiences with qualitative researchers licensing our text analytics software, OdinText.

The Problem with Qualitative Research

IF Qual research was really used in the way it’s often positioned, ‘as a way to inform quant research’, that would be ok. The fact of the matter is though, Qual often isn’t being used that way, but instead as an end in and of itself. Let me explain.

First, there is one exception to this rule of only using Qual as pilot feedback for Quant. If you had a product for instance which was specifically made only for US State Governors, then your total population is only N=50. And of course it is highly unlikely that you would ever get all the Governors of each and every US State to participate in any research (which would be a census of all governors), and so if you were fortunate enough to have a group of say 5 Governors whom were willing to give you feedback on your product or service, you would and should obviously hang on to and over analyze every single comment they gave you.

IF however you have even a slightly more common mainstream product, I’ll take a very common product like hamburgers as an example, and you are relying on 5-10 focus groups of n=12 to determine how different parts of the USA (North East, Mid-West, South and West) like their burgers, and rather than feeding  directly into some quantitative research instrument with a greater sample, you issue a ‘Report’ that you share with management; well then you’ve probably just wasted a lot of time and money for some extremely inaccurate and dangerous findings. Yet surprisingly, this happens far more often than one would imagine.

Cognitive Dissonance Among Qual Researchers when Using OdinText

How do I know this you may ask? Good Text Analytics software is really about data mining and pattern recognition. When I first launched OdinText we had a lot of inquiries from Qualitative researchers who wanted some way to make their lives easier. After all, they had “a lot” of unstructured/text comment data which was time consuming for them to process, read, organize and analyze. Certainly, software made to “Analyze Text” must therefore be the answer to their problems.

The problem was that the majority of Qual researchers work with tiny projects/sample, interviews and groups between n=1 and n=12. Even if they do a couple of groups like in the hamburger example I gave above, we’re still taking about a total of just around n=100 representing four or more regional groups of interest, and therefore fewer than n=25 per group. It is impossible to get meaningful/statistically comparable findings and identify real patterns between the key groups of interest in this case.

The Little Noticed Trend In Qual (Qual Data is Getting Bigger)

However, slowly across the past couple of years or so, for the first time I’ve seen a movement of some ‘Qualitative’ shops and researchers, toward Quant. They have started working with larger data sets than before. In some cases, it has been because they have been pulled in to manage larger ongoing community/boards, in some cases larger social media projects, and in others, they have started using survey data mixed with qual, or even better, employing qualitative techniques in quant research (think better open-ends in survey research).

For this reason, we now have a small but growing group of ‘former’ Qual researchers using OdinText. These researchers aren’t our typical mixed data or quantitative researchers, but qualitative researchers that are working with larger samples.

And guess what, “Qualitative” has nothing to do with whether data is in text or numeric format, instead it has everything to so with sample size. And so perhaps unknowingly, these ‘Qualitative Researchers’ have taken the step across the line into Quantitative territory, where often for the first time in their career, statistics can actually be used. – And it can be shocking!

My Experience with ‘Qualitative’ Researchers going Quant/using Text Analytics

Let me explain what I mean. Recently several researchers that come from a clear ‘Qual’ background have become users of our software OdinText. The reason is that the amount of data they had was quickly getting “bigger than they were able to handle”. They believe they are still dealing with “Qualitative” data because most of it is text based, but actually because of the volume, they are now Quant researchers whether they know it or not (text or numeric data is irrelevant).

Ironically, for this reason, we also see much smaller data sizes/projects than ever before being uploaded to the OdinText servers. No, not typically single focus groups with n=12 respondents, but still projects that are often right on the line between quant and qual (n=100+).

The discussions we’re having with these researchers as they begin to understand the quantitative implications of what they have been doing for years are interesting.

Let me preface this with the fact that I have a great amount of respect for the ‘Qualitative’ researchers that begin using OdinText. Ironically, the simple fact that we have mutually determined that an OdinText license is appropriate for them means that they are no longer ‘Qualitative’ researchers (as I explained earlier). They are in fact crossing the line into Quant territory, often for the first time in their careers.

The data may be primarily text based, though usually mixed, but there’s no doubt in their mind nor ours, that one of the most valuable aspects of the data is the customer commentary in the text, and this can be a strength

The challenge lies in getting them to quickly accept and come to terms with quantitative/statistical analysis, and thereby also the importance of sample size.

What do you mean my sample is too small?

When you have licensed OdinText you can upload pretty much any data set you have. So even though they may have initially licensed OdinText to analyze some projects with say 3,000+ comments, there’s nothing to stop them from uploading that survey or set of focus groups with just n=150 or so.

Here’s where it sometimes gets interesting. A sample size of n=150 is right on the borderline. It depends on what you are trying to do with it of course. If half of your respondents are doctors (n=75) and half are nurses (n=75), then you may indeed be able to see some meaningful differences between these two groups in your data.

But what if these n=150 respondents are hamburger customers, and your objective was to understand the difference between the 4 US regions in the I referenced earlier? Then you have about n=37 in each subgroup of interest, and you are likely to have very few, IF ANY, meaningful patterns or differences.

Here’s where that cognitive dissonance can happen --- and the breakthroughs if we are lucky.

A former ‘Qual Researcher’ who has spent the last 15 years of their career making ‘management level recommendations’ on how to market burgers differently in different regions based on data like this, for the first time is looking at software which says that there are maybe just two to 3 small differences, or even worse, NO MEANINGFUL PATTERNS OR DIFFERENCES WHATSOEVER, in their data, may be in shock!

How can this be? They’ve analyzed data like this many times before, and they were always able to write a good report with lots of rich detailed examples of how North Eastern Hamburger consumers preferred this or that because of this and that. And here we are, looking at the same kind of data, and we realize, there is very little here other than completely subjective thoughts and quotes.

Opportunity for Change

This is where, to their credit, most of our users start to understand the quantitative nature of data analysis. They, unlike the few ‘Quant Only Jockie’s’ I referenced at the beginning of the article already understand that many of the best insights come from text data in free form unaided, non-leading, yet creative questions.

They only need to start thinking about their sample sizes before fielding a project. To understand the quantitative nature of sampling. To think about the handful of structured data points that they perhaps hadn’t thought much about in previous projects and how they can be leveraged together with the unstructured data. They realize they need to start thinking about this first, before the data has all been collected and the project is nearly over and ready for the most important step, the analysis, where rubber hits the road and garbage in really should mean garbage out.

If we’re lucky, they quickly understand, its not about Quant and Qual any more. It’s about Mixed Data, it’s about having the right data, it’s about having enough data to generate robust findings and then superior insights!

Final Thoughts on the Two Meaningless Nearly Terms of ‘Quant and Qual’

As I’ve said many times before here and on the NGMR blog, the terms “Qualitative” and “Quantitative” at least the way they are commonly used in marketing research, is already passé.

The future is Mixed Data. I’ve known this to be true for years, and almost all our patent claims involve this important concept. Our research shows time and time again, that when we use both structured and unstructured data in our analysis, models and predictions, the results are far more accurate.

For this reason we’ve been hard at work developing the first ever truly Mixed Data Analytics Platform, we’ll be officially launching it three months from now, but many of our current customers already have access. [For those who are interested in learning more or would like early access you can inquire here: OdinText.com/Predict-What-Matters].

In the meantime, if you’re wondering whether you have enough data to warrant advanced mixed data and text annalysis, check out the online version of article in QRCA Views magazine here. Robin Wedewer at QRCA really did an excellent job in asking some really pointed questions that forced me too answer more honestly and clearly than I might otherwise have.

I realize not everyone will agree with today’s post nor my interview with QRCA, and I welcome your comments here. I just please ask that you read both the post above, as well as the interview in QRCA before commenting solely based on the title of this post.

Thank you for reading. As always, I welcome questions publicly in post below or privately via LinkedIn or our Inquiry form.

@TomHCAnderson

Artificial Intelligence in Consumer Insights

A Q&A session with ESOMAR’s Research World on Artificial Intelligence, Machine Learning, and implications in Marketing Research  [As part of an ESOMAR Research World article on Artificial Intelligence OdinText Founder Tom H. C. Anderson was recently took part in a Q&A style interview with ESOMAR’s Annelies Verheghe. For more thoughts on AI check out other recent posts on the topic including Why Machine Learning is Meaningless, and Of Tears and Text Analytics. We look forward to your thoughts or questions via email or in the comments section.]

 

ESOMAR: What is your experience with Artificial Intelligence & Machine Learning (AI)? Would you describe yourself as a user of AI or a person with an interest in the matter but with no or limited experience?

TomHCA: I would describe myself as both a user of Artificial Intelligence as well as a person with a strong interest in the matter even though I have limited mathematical/algorithmic experience with AI. However, I have colleagues here at OdinText who have PhD's in Computer Science and are extremely knowledgeable as they studied AI extensively in school and used it elsewhere before joining us. We continue to evaluate, experiment, and add AI into our application as it makes sense.

ESOMAR: For many people in the research industry, AI is still unknown. How would you define AI? What types of AI do you know?

TomHCA: Defining AI is a very difficult thing to do because people, whether they are researchers, data scientists, in sales, or customers, they will each have a different definition. A generic definition of AI is a set of processes (whether they are hardware, software, mathematical formulas, algorithms, or something else) that give anthropomorphically cognitive abilities to machines. This is evidently a wide-ranging definition. A more specific definition of AI pertaining to Market Research, is a set of knowledge representation, learning, and natural language processing tools that simplifies, speeds up, and improves the extraction of meaningful data.

The most important type of AI for Market Research is Natural Language Processing. While extracting meaningful information from numerical and categorical data (e.g., whether there is a correlation between gender and brand fidelity) is essentially an easy and now-solved problem, doing the same with text data is much more difficult and still an open research question studied by PhDs in the field of AI and machine learning. At OdinText, we have used AI to solve various problems such as Language Detection, Sentence Detection, Tokenizing, Part of Speech Tagging, Stemming/Lemmatization, Dimensionality Reduction, Feature Selection, and Sentence/Paragraph Categorization. The specific AI and machine learning algorithms that we have used, tested, and investigated range a wide spectrum from Multinomial Logit to Principal Component Analysis, Principal Component Regression, Random Forests, Minimum Redundancy Maximum Relevance, Joint Mutual Information, Support Vector Machines, Neural Networks, and Maximum Entropy Modeling.

AI isn’t necessarily something everyone needs to know a whole lot about. I blogged recently, how I felt it was almost comical how many were mentioning AI and machine learning at MR conferences I was speaking at without seemingly any idea what it means. http://odintext.com/blog/machine-learning-and-artificial-intelligence-in-marketing-research/

In my opinion, a little AI has already found its way into a few of the applications out there, and more will certainly come. But, if it will be successful, it won’t be called AI for too long. If it’s any good it will just be a seamless integration helping to make certain processes faster and easier for the user.

ESOMAR: What concepts should people that are interested in the matter look into?

TomHCA: Unless you are an Engineer/Developer with a PhD in Computer Science, or someone working closely with someone like that on a specific application, I’m not all that sure how much sense it makes for you to be ‘learning about AI’. Ultimately, in our applications, they are algorithms/code running on our servers to quickly find patterns and reduce data.

Furthermore, as we test various algorithms from academia, and develop our own to test, we certainly don’t plan to share any specifics about this with anyone else. Once we deem something useful, it will be incorporated as seamlessly as possible into our software so it will benefit our users. We’ll be explaining to them what these features do in layman’s terms as clearly as possible.

I don’t really see a need for your typical marketing researcher to know too much more than this in most cases. Some of the algorithms themselves are rather complex to explain and require strong mathematical and computer science backgrounds at the graduate level.

ESOMAR: Which AI applications do you consider relevant for the market research industry? For which task can AI add value?

TomHCA: We are looking at AI in areas of Natural Language Processing (which includes many problem subsets such as Part of Speech Tagging, Sentence Detection, Document Categorization, Tokenization, and Stemming/Lemmatization), Feature Selection, Data Reduction (i.e., Dimensionality Reduction) and Prediction. But we've gone well beyond that. As a simple example, take key driver analysis. If we have a large number of potential predictors, which are the most important in driving a KPI like customer satisfaction?

ESOMAR: Can you share any inspirational examples from this industry or related industries (advertisement, customer service)  that can illustrate these opportunities

TomHCA: As one quick example, a user of OdinText I recently spoke to used the software to investigate what text comments were most likely to drive belonging into either of several predefined important segments. The nice thing about AI is that it can be very fast. The not so nice thing is that sometimes at first glance some of the items identified, the output, can either be too obvious, or on the other extreme, not make any sense whatsoever.  The gold is in the items somewhere in the middle. The trick is to find a way for the human to interact with the output which gives them confidence and understanding of the results.

a human is not capable of correctly analyzing thousands, 100s of thousands, or even millions of comments/datapoints, whereas AI will do it correctly in a few seconds. The downside of AI is that some outcomes are correct but not humanly insightful or actionable. It’s easier for me to give examples when it didn’t work so well since its hard for me to share info on how are clients are using it. But for instance recently AI found that people mentioning ‘good’ 3 times in their comments was the best driver of NPS score – this is evidently correct but not useful to a human.

In another project a new AI approach we were testing reported that one of the most frequently discussed topics was “Colons”. But this wasn’t medical data! Turns out the plural of Colon is Cola, I didn’t know that. Anyway, people were discussing Coca-Cola, and AI read that as Colons…  This is exactly the part of AI that needs work to be more prevalent in Market Research.”

Since I can’t talk about too much about how our clients use our software on their data, In a way it’s easier for me to give a non-MR example. Imagine getting into a totally autonomous car (notice I didn’t have to use the word AI to describe that). Anyway, you know it’s going to be traveling 65mph down the highway, changing lanes, accelerating and stopping along with other vehicles etc.

How comfortable would you be in stepping into that car today if we had painted all the windows black so you couldn’t see what was going on?  Chances are you wouldn’t want to do it. You would worry too much at every turn that you might be a casualty of oncoming traffic or a tree.  I think partly that’s what AI is like right now in analytics. Even if we’ll be able to perfect the output to be 100 or 99% correct, without knowing what/how we got there, it will make you feel a bit uncomfortable.  Yet showing you exactly what was done by the algorithm to arrive at the solution is very difficult.

Anyway, the upside is that in a few years perhaps (not without some significant trial and error and testing), we’ll all just be comfortable enough to trust these things to AI. In my car example, you’d be perfectly fine getting into an Autonomous car and never looking at the road, but instead doing something else like working on your pc or watching a movie.

The same could be true of a marketing research question. Ultimately the end goal would be to ask the computer a business question in natural language, written or spoken, and the computer deciding what information was already available, what needed to be gathered, gathering it, analyzing it, and presenting the best actionable recommendation possible.

ESOMAR: There are many stories on how smart or stupid AI is. What would be your take on how smart AI Is nowadays. What kind of research tasks can it perform well? Which tasks are hard to take over by bots?

TomHCA: You know I guess I think speed rather than smart. In many cases I can apply a series of other statistical techniques to arrive at a similar conclusion. But it will take A LOT more time. With AI, you can arrive at the same place within milliseconds, even with very big and complex data.

And again, the fact that we choose the technique based on which one takes a few milliseconds less to run, without losing significant accuracy or information really blows my mind.

I tell my colleagues working on this that hey, this can be cool, I bet a user would be willing to wait several minutes to get a result like this. But of course, we need to think about larger and more complex data, and possibly adding other processes to the mix. And of course, in the future, what someone is perfectly happy waiting for several minutes today (because it would have taken hours or days before), is going to be virtually instant tomorrow.

ESOMAR: According to an Oxford study, there is a 61% chance that the market research analyst job will be replaced by robots in the next 20 years. Do you agree or disagree? Why?

TomHCA: Hmm. 20 years is a long time. I’d probably have to agree in some ways. A lot of things are very easy to automate, others not so much.

We’re certainly going to have researchers, but there may be fewer of them, and they will be doing slightly different things.

Going back to my example of autonomous cars for a minute again. I think it will take time for us to learn, improve and trust more in automation. At first autonomous cars will have human capability to take over at any time. It will be like cruise control is now. An accessory at first. Then we will move more and more toward trusting less and less in the individual human actors and we may even decide to take the ability for humans to intervene in driving the car away as a safety measure. Once we’ve got enough statistics on computers being safe. They would have to reach a level of safety way beyond humans for this to happen though, probably 99.99% or more.

Unlike cars though, marketing research usually can’t kill you. So, we may well be comfortable with a far lower accuracy rate with AI here.  Anyway, it’s a nice problem to have I think.

ESOMAR: How do you think research participants will react towards bot researchers?

TomHCA: Theoretically they could work well. Realistically I’m a bit pessimistic. It seems the ability to use bots for spam, phishing and fraud in a global online wild west (it cracks me up how certain countries think they can control the web and make it safer), well it’s a problem no government or trade organization will be able to prevent from being used the wrong way.

I’m not too happy when I get a phone call or email about a survey now. But with the slower more human aspect, it seems it’s a little less dangerous, you have more time to feel comfortable with it. I guess I’m playing devil’s advocate here, but I think we already have so many ways to get various interesting data, I think I have time to wait RE bots. If they truly are going to be very useful and accepted, it will be proven in other industries way before marketing research.

But yes, theoretically it could work well. But then again, almost anything can look good in theory.

ESOMAR: How do you think clients will feel about the AI revolution in our industry?

TomHCA: So, we were recently asked to use OdinText to visualize what the 3,000 marketing research suppliers and clients thought about why certain companies were innovative or not in the 2017 GRIT Report. One of the analysis/visualizations we ran which I thought was most interesting visualized the differences between why clients claimed a supplier was innovative VS why a supplier said these firms were innovative.

I published the chart on the NGMR blog for those who are interested [ http://nextgenmr.com/grit-2017 ], and the differences couldn’t have been starker. Suppliers kept on using buzzwords like “technology”, “mobile” etc. whereas clients used real end result terms like “know how”, "speed" etc.

So I’d expect to see the same thing here. And certainly, as AI is applied as I said above, and is implemented, we’ll stop thinking about it as a buzz word, and just go back to talking about the end goal. Something will be faster and better and get you something extra, how it gets there doesn’t matter.

Most people have no idea how a gasoline engine works today. They just want a car that will look nice and get them there with comfort, reliability and speed.

After that it’s all marketing and brand positioning.

 

[Thanks for reading today. We’re very interested to hear your thoughts on AI as well. Feel free to leave questions or thoughts below, request info on OdinText here, or Tweet to us @OdinText]

When Oprah is President We Can Celebrate Family Day While Skiing!

Text Analytics Poll™ Shows What We’d Like Instead of Presidents Day It’s been less than a week since our Valentine’s Day poll unearthed what people dislike most about their sweethearts, and already another holiday is upon us! Though apparently for most of us it’s not much of a holiday at all; well over half of Americans say they do nothing to commemorate ‘Presidents Day.’

You’ll note I put the holiday in single quotes. That’s because there’s some confusion around the name. Federally, it’s recognized as Washington’s Birthday. At the state level, it’s known by a variety of names—President’s Day, Presidents’ Day, Presidents Day and others, again, depending on the state.

But the name is not the only inconsistency about Presidents Day. If you’re a federal employee OR you happen to be a state employee in a state where the holiday is observed OR you work for an employer who honors it, you get the day off work with pay. Schools may or may not be closed, but that again depends on where you live.

As for what we’re observing exactly, well, that also depends on the state, but people generally regard the holiday as an occasion to honor either George Washington, alone, or Washington and Abraham Lincoln, or U.S. presidents, in general.

Perhaps the one consistent aspect of this holiday is the sales? It’s particularly popular among purveyors of automobiles, mattresses, and furniture.

Yes, it’s a patriotic sort of holiday, but on the whole, we suspected that ‘Presidents Day’ fell on the weaker end of the American holiday spectrum, so we investigated a little bit…

About this Text Analytics Poll™

In this example for our ongoing series demonstrating the efficiency, flexibility, and practicality of the Text Analytics Poll™ for insights generation, we opted for a light-hearted poll using a smaller sample* than usual. While text analytics have obvious value when applied to larger-scale data where human reading or coding is impossible or too expensive, you’ll see here that OdinText also works very effectively with smaller samples!

I’ll also emphasize that the goal of these little Text Analytics Polls™ is not to conduct a perfect study, but to very quickly design and field a survey with only one open-ended question, analyze the results with OdinText, and report the findings in here on this blog. (The only thing that takes a little time—usually 2-3 days—is the data collection.)

So while the research is representative of the general online population, and the text analytics coding applied with 100% consistency throughout the entire sample, this very speedy exercise is meant to inspire users of OdinText to use the software in new ways to answer new questions. It is not meant to be an exhaustive exploration of the topic. We welcome our users to comment and suggest improvements in the questions we ask and make suggestions for future topics!

Enough said, on to the results…

A Holiday In Search of a Celebrant in Search of a Holiday…

Poll I: Americans Celebrate on the Slopes, Not in Stores

When we asked Americans how they typically celebrate Presidents Day, the vast majority told us they don’t. And those few of us lucky enough to have the day off from work tend to not do much outside of sleeping.

But the surprise came from those few who actually said they do something on Presidents Day!

We expected people to say they go shopping on Presidents Day, but the most popular activity mentioned (after nothing and sleeping) was skiing! And skiing was followed by 2) barbecuing and 3) spending time with friends—not shopping.

Poll II: Change it to Family Day?

So, maybe as far as holidays go, Presidents Day is a tad lackluster? Could we do better?

We asked Americans:

Q. If we could create a new holiday instead of Presidents Day, what new holiday would you suggest we celebrate?

While some people indicated Presidents Day is fine as is, among those who suggested a new holiday there was no shortage of creativity!

The three most frequently mentioned ideas by large margins for replacement of Presidents Day were 1) Leaders/Heroes Day, 2) Native American Day (this holiday already exists, so maybe it could benefit from some publicity?) and 3) Family Day (which is celebrated in parts of Canada and other countries).

People also seemed to like the idea of shifting the date and making a holiday out of other important annually occurring events that lent themselves to a day off in practical terms like Election Day, Super Bowl Monday and, my personal favorite, Taxpayer Day on April 15!

Poll III: From Celebrity Apprentice to Celebrity POTUS

Donald Trump isn’t the first person in history to have not held elected office before becoming president, but he is definitely the first POTUS to have had his own reality TV show! Being Presidents Day, we thought it might be fun to see who else from outside of politics might interest Americans…

 Q: If you could pick any celebrity outside of politics to be President, who would it be?

 

Looks like we could have our first female president if Oprah ever decides to run. The media mogul’s name just rolled off people’s tongues, followed very closely by George Clooney, with Morgan Freeman in a respectable third.

Let Them Tell You in Their Own Words

In closing, I’ll remind you that none of these data were generated by a multiple-choice instrument, but via unaided text comments from people in their own words.

What never ceases to amaze me about these exercises is how even when we give people license to say whatever crazy thing they can think up—without any prompts or restrictions—people often have the same thoughts. And so open-ends lend themselves nicely to quantification using a platform like OdinText.

If you’re among the lucky folks who have the holiday off, enjoy the slopes!

Until next time, Happy Presidents Day!

@TomHCAnderson

PS.  Do you have an idea for our next Text Analytics Poll™? We’d love to hear from you. Or, why not use OdinText to analyze your own data!

[*Today’s OdinText Text Analytics PollTM sample of n=500 U.S. online representative respondents has been sourced through of Google Surveys. The sample has a confidence interval of +/- 4.38 at the 95% Confidence Level. Larger samples have a smaller confidence level. Subgroup analyses within the sample have a larger confidence interval.]

About Tom H. C. Anderson

Tom H. C. Anderson is the founder and managing partner of OdinText, a venture-backed firm based in Stamford, CT whose eponymous, patented SAS platform is used by Fortune 500 companies like Disney, Coca-Cola and Shell Oil to mine insights from complex, unstructured and mixed data. A recognized authority and pioneer in the field of text analytics with more than two decades of experience in market research, Anderson is the recipient of numerous awards for innovation from industry associations such as CASRO, ESOMAR, and the ARF. He was named one of the “Four under 40” market research leaders by the American Marketing Association in 2010. He tweets under the handle @tomhcanderson

OdinText Predicts What Television Shows You Will Like

How Text Analytics Rescued Me from a #ShowHole!  My wife and I recently found ourselves in the uncomfortable condition commonly known as a “show hole.” Are you familiar with this?

A show hole refers to the state of loss and aimlessness one experiences after completing—often via binge-watching—the last episode in a beloved TV series without having a successor program queued up. The term was popularized by an Amazon Fire campaign a couple years ago, and you’ll find it hashtagged all over social media these days by people desperately in need of relief.

The show hole is an interesting phenomenon that speaks to how dramatically audience consumption habits have changed with the advent of the DVR, streaming and on-demand services like Netflix, Hulu, Amazon, etc. But what’s curious to me is how such a clearly great need continues to go relatively unmet.

Of course, subscribers to on-demand services have help in the way of recommendations algorithms. Netflix, in particular, has famously invested extensively in developing predictive analytics to suggest other shows to watch.

Still, the preponderance of cries for help on social media would seem to indicate that for many people these solutions have fallen short.

Indeed, it appears that people tend to prefer recommendations from other people, which introduces a different set of problems.

The Problem with Recommendations, Ratings and Reviews from People

In my own disappointing search for what to watch next, I found #showhole aplenty on Twitter, but the platform doesn’t lend itself well to discussion, so most of those who tweet about it get left hanging. Usually it’s just a “Help! I’m in a #showhole!” message from someone after finishing a series, but hardly anyone tweets a reply with suggestions.

Note: Because Twitter isn’t well-suited to this kind of interaction, your standard social media monitoring tool—most of them rely on Twitter data—wouldn’t be effective for the type of analysis we’ll cover today.

I did, however, find a ton of recommendation activity occurring on Facebook, Reddit and a variety of other discussion boards and community-based sites, including surprising places like babycenter.com—a support community for pregnant women and new moms—replete with threads where members actually recommend series’ for other members to try next.

This Yelp-like model for getting out of a show hole has its own limitations, though. How do I know I’ll like what you like? Is it enough to assume that since we’re both new moms that we’ll enjoy the same shows? Or if we both enjoyed one program, that I’ll enjoy whatever else you’ve watched? Similarly, if I ask you on a scale of 1-10 to rate a show, how would that information be useful to me if we don’t have the same tastes? Remember also that we’re looking across genres. Our tastes in dramas might be similar, while our tastes in comedies could be worlds apart.

In short, we have all of these people providing recommendations online, but the recommendations really aren’t any more helpful to the prospective viewer than star-based ratings and reviews. I.e., the show hole sufferer is forced to audition each of these programs until he/she finds one that fits—a time-consuming and potentially frustrating process!

How Text Analytics Can Make These Recommendations Useful

As I pondered the recommendations I saw online, it occurred to me that if I could apply text analytics to identify preference patterns based on recommendations from a broad enough swath of people, I might arrive at a recommendation suited to the unique tastes of my wife and I that we could then invest time in with a high confidence level.

Happily, I discovered that when suggesting new shows to watch via social media, people tend to provide more than one recommendation, and these recommendations usually are not limited to a single genre. This means we have sufficient preference data at the individual level, which, if aggregated from enough people, could form the basis for a predictive model.

In a very short time period, I was able to scrape (collect) several thousand recommendations across a variety of sources online. It’s worth noting that just about every single network that the average American has access to was represented in this data. This is important because someone who uses HBO GO, for example, is obviously more likely to watch and recommend programming from that network than someone does not subscribe to it.

We then layered predictive analytics atop the data using OdinText to see whether text analytics could solve my show hole dilemma. Specifically, I wanted to see what other shows are most frequently co-occurring with shows that my wife and I like in these online recommendations. (OdinText has a few ways it can help in cases like this, including the co-occurrence visualization covered in a recent post on this blog by my colleague, Gosia Skorek, here.)

It’s also important to emphasize here that we accomplished this analysis without asking a single question of anyone, although this type of data could be very nicely augmented with survey data.

OdinText Rescues Tom and His Wife from Their Show Hole!

This data was more challenging than I expected, but OdinText enabled us to find a model that delivered!

Below you’ll find examples of preference clouds based on the co-occurrence of mentions harvested from several thousand recommendations across discussion boards and other social media (excluding Twitter).

Essentially, you’re seeing OdinText’s recommendation for what you should watch next based on the series you’ve just completed.

In our case, my wife and I had completed the most recent episode of “The Walking Dead” on AMC—now on hiatus through February—and, as you can see, OdinText recommended we watch “Goliath” on Amazon.

Not only had I never heard of this series, but when I looked it up I was skeptical that we’d enjoy it because my wife and I are not particularly fond of legal dramas.

It turned out that OdinText’s prediction was spot on; we’re both hooked on “Goliath”!

I'll probably check out "Drunk History" on Comedy Central next...

Attention Show Hole Sufferers: Let OdinText Get You Out!

I think this exercise demonstrates the versatility of the OdinText platform. With a little creativity, OdinText can not only provide breakthrough consumer insights, but solve problems of all stripes.

Here are a few more examples. You’ll note that quite often the suggestions cut across networks. Even though obviously someone recommending something on HBO will be more likely to have seen and to recommend other shows on that network, the model often makes suggestions that are quite surprising, cutting across networks and time. Here are just a few:

Above we have OdinText’s recommendations for anyone who likes “Luke Cage.” (I haven’t seen it and typically am not a fan of super hero stuff, but I ran it as I saw in the data that the show was very popular) “Luke Cage” fans might also like “Daredevil,” “Stranger Things” (which I did love), and “The Flash.” The first three here are all on Netflix, the last one is on CW.

You don’t have to be a premium channel streaming snob to benefit here. If you like the popular sitcom “Big Bang Theory” on CBS, you may well also like their new “2 Broke Girls”, and “Last Man Standing” or “Modern Family” on ABC.

Some of the best shows, in my opinion, are often also ironically less popular and less frequently mentioned. Two such shows are HBO’s “Deadwood,” for which OdinText recommended one good fit—Poldark,” a BBC series--and Netflix’s “Peaky Blinders,” for which OdinText suggests trying “Downton Abbey.

I was honestly so impressed with OdinText’s recommendations that I’m entertaining building a suggestion app based on this model. (And unlike Netflix, I didn’t need dozens of developers and millions of dollars to get the right answer.)

I may also refine the underlying model a bit, as well as update the underlying data in a few months when there are enough new series being mentioned to make doing so worthwhile.

In the meantime, I feel obliged to offer immediate assistance to those poor souls in the throes of a show hole today!

If you’re stuck in a show hole, post the title of your recent favorite series in the comment section of today’s blog. OdinText will tell the first 10 people who respond what to watch next. Then come back and tell us how OdinText did.

I look forward to your comments!

@TomHCanderson

Ps. See firsthand how OdinText can help you learn what really matters to your customers and predict real behavior. Contact us for a demo using your own data here!

About Tom H. C. Anderson

Tom H. C. Anderson is the founder and managing partner of OdinText, a venture-backed firm based in Stamford, CT whose eponymous, patented SAS platform is used by Fortune 500 companies like Disney, Coca-Cola and Shell Oil to mine insights from complex, unstructured and mixed data. A recognized authority and pioneer in the field of text analytics with more than two decades of experience in market research, Anderson is the recipient of numerous awards for innovation from industry associations such as CASRO, ESOMAR and the ARF. He was named one of the "Four under 40" market research leaders by the American Marketing Association in 2010. He tweets under the handle @tomhcanderson.

What Does the Co-Occurence Graph Tell You?

Text Analytics Tips - Branding What does the co-occurrence graph tell you?Text Analytics Tips by Gosia

The co-occurrence graph in OdinText may look simple at first sight but it is in fact a very complex visualization. Based on an example we are going to show you how to read and interpret this graph. See the attached screenshots of a single co-occurrence graph based on a satisfaction survey of 500 car dealership customers (Fig. 1-4).

The co-occurrence graph is based on multidimensional scaling techniques that allow you to view the similarity between individual cases of data (e.g., automatic terms) taking into account various aspects of the data (i.e., frequency of occurrence, co-occurrence, relationship with the key metric). This graph plots the co-occurrence of words represented by the spatial distance between them, i.e., it plots as well as it can terms which are often mentioned together right next to each other (aka approximate overlap/concurrence).

Figure 1. Co-occurrence graph (all nodes and lines visible).

The attached graph (Fig. 1 above) is based on 50 most frequently occurring automatic terms (words) mentioned by the car dealership customers. Each node represents one term. The node’s size corresponds to the number of occurrences, i.e., in how many customer comments a given word was found (the greater node’s size, the greater the number of occurrences). In this example, green nodes correspond to higher overall satisfaction and red nodes to lower overall satisfaction given by customers who mentioned a given term, whereas brown nodes reflect satisfaction scores close to the metric midpoint. Finally, the thickness of the line connecting two nodes highlights how often the two terms are mentioned together (aka actual overlap/concurrence); the thicker the line, the more often they are mentioned together in a comment.

Figure 2. Co-occurrence graph (“unprofessional” node and lines highlighted).

So what are the most interesting insights based on a quick look at the co-occurrence graph of the car dealership customer satisfaction survey?

  • “Unprofessional” is the most negative term (red node) and it is most often mentioned together with “manager” or “employees” (Fig. 2 above).
  • “Waiting” is a relatively frequently occurring (medium-sized node) and a neutral term (brown node). It is often mentioned together with “room” (another neutral term) as well as “luxurious”, “coffee”, and “best”, which are corresponding to high overall satisfaction (light green node). Thus, it seems that the luxurious waiting room with available coffee is highly appreciated by customers and makes the waiting experience less negative (Fig. 3 below).
  • The dealership “staff” is often mentioned together with such positive terms as “always”, “caring”, “nice”, “trained”, and “quick” (Fig. 4 below). However, staff is also mentioned with more negative terms including “unprofessional”, “trust”, “helpful” suggesting a few negative customer evaluations related to these terms which may need attention and improvement.

    Figure 3. Co-occurrence graph (“waiting” node and lines highlighted).

    Figure 4. Co-occurrence graph (“staff” node and lines highlighted).

    Hopefully, this quick example can help you extract quick and valuable insights based on your own data!

Gosia

Text Analytics Tips with Gosi

[NOTE: Gosia is a Data Scientist at OdinText Inc. Experienced in text mining and predictive analytics, she is a Ph.D. with extensive research experience in mass media’s influence on cognition, emotions, and behavior.  Please feel free to request additional information or an OdinText demo here.]

Can Text Analytics Shed Light On Trump's Appeal?

From tacit to more explicit insights, text analytics helps answer the why’s in voting

Because of the interest in yesterday’s post I decided to continue on the topics of politics today.  As a marketing researcher and data scientist though I found yesterday’s analysis a bit more interesting. Not because of the findings per se, but because we were able to use text analytics to accurately predict real attitudes and behavior by not just ‘reading between the lines’ but extrapolating a relationship between seemingly non related attitudes and opinions, which of course are related and predictive when you look more closely.

Of course text analytics can be interesting when used on more explicit data as well. So today I’ll take a look at two more open ended comment questions two different surveys.

In case you're wondering, the benefit of a text answer rather than asking several structured survey questions with rating scales is that unaided text questions give a much truer measure of what issues are actually important to a respondent. Rating scale questions force respondents to have an opinion on issues even when there is none, and thus structured survey questions (even the popular ones like Net Promoter Score) are usually far less effective in predicting actual behavior than text data in our experience.

Reason for Political Affiliation

Immediately after the self-description exercise in yesterday’s analysis we obviously needed to ask what the respondents political affiliation was (so that we could understand what relationship, if any, there is between how we view ourselves and political affiliation).

Respondents were able to designate which party if any they were affiliated with, whether they considered themselves Independent, Tea Party, Green, or something else, and why?

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WhyDemRep

WhyDemRep

The ability to get a good quantitative relative measure to a why question is something unique to text analytics. Perhaps surprisingly there were rather few mentions of specific campaign issues. Instead the tendency was to use far more general reasons to explain why one votes a certain way.

While Republicans and Democrats are equally unlikely to mention “Conservatism’ and “Liberalism” when describing themselves (from yesterday's post), Republicans are about twice as likely to say they are affiliated with the Republican party because of their “Conservative” values (11% VS 5% “liberal” for Democrats).

Democrats say they vote the way they do because the Democratic party is “For the People”, “Cares about the Poor” and “the Middle [and] working class”.

Republicans on the other hand say they vote Republican because of “values” especially the belief that “you have to work for what you get”. Many also mention “God” and/or their “Christian” Faith as the reason. The desire for smaller/less government and greater Military/Defense spending are also significant reasons for Republicans.

Of course we could have probed deeper in the OE comments with a second question if we had wanted to. Still it is telling that specific issues like Healthcare, Education, Gay Rights and Taxes are less top-of-mind among voters than these more general attitudes about which party is right for them.

Describe Your Ideal President

As mentioned earlier we are looking toward social media to understand and build models. Therefore, we also recently asked a separate sample of n=1000 Americans, all who are active on Twitter, what qualities they felt the President of the United States (POTUS) should have.

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TextAnalyticsPOTUS

TextAnalyticsPOTUS

The chart above is divided by those who said they tend to vote or at least typically skew toward that respective party.

The findings do help explain the current political climate a bit. Both Democrats and Republicans were most likely to mention “honesty” as a quality they look for, perhaps indicating a greater frustration with politics in general. The idea of “honesty” though is more important to voters who skew toward the GOP.

Those who favor the Democratic party are significantly more likely to value traits like Intelligence, Compassion/Empathy, skill, educational attainment of the candidate and open-mindedness.

Those who lean Republican however are significantly more likely to value a candidate who is perceived both as a strong leader in general, but also more specifically is strongly for America. Rather than educational attainment, softer more tacit skills are valued by this group, for instance Republican voters put greater emphasis on experience and “know how”. Not surprisingly, based on yesterday’s data on how voters view themselves, Republican voters also value Family values and Christian faith in their ideal POTUS.

Research has shown that people prefer leaders similar to themselves. Looking back to some of the self descriptions in yesterday's data we definitely see a few similarities in the above...

Thanks for all the feedback on yesterday’s post. Please join me week after next when I plan on sharing some more interesting survey findings not related to politics, but of course to text analytics.

@TomHCAnderson

WhyVoteTextAnalytics

WhyVoteTextAnalytics

Tom H.C. Anderson

Tom H.C. Anderson

To learn more about how OdinText can help you understand what really matters to your customers and predict actual behavior,  please contact us or request a Free Software Demo here >

[NOTE: Tom H. C. Anderson is Founder of Next Generation Text Analytics software firm OdinText Inc. Click here for more Text Analytics Tips]

Text Analysis Predicts Your Politics Without Asking

How What You Say Says Way More Than What You Said

Pretend for a moment that you had a pen pal overseas and they asked you to describe yourself. What would you tell them? What makes you “you”?

It turns out that which traits, characteristics and aspects of your identity you choose to focus on may say more than you realize.

For instance, they can be used to predict whether you are a Democrat or a Republican.

With the U.S. presidential race underway in earnest, I thought it would be interesting to explore what if any patterns in the way people describe themselves could be used to identify their political affiliation.

So we posed the question above verbatim to a nationally representative sample of just over n=1000 (sourced via CriticalMix) and ran the responses through OdinText.

Not surprisingly, responses to this open-ended question were as varied as the people who provided them, but OdinText was nevertheless able to identify several striking and statistically significant differences between the way Republicans and Democrats described themselves.

NOT About Demographics

Let me emphasize that this exercise had nothing to do with demographics. We’re all aware of the statistical demographic differences between Republicans and Democrats.

For our purposes, what if any specific demographic information people shared in describing themselves was only pertinent to the extent that it constituted a broader response pattern that could predict political affiliation.

For example, we found that Republicans were significantly more likely than Democrats to say they have blonde hair.

Of course, this does not necessarily mean that someone with blonde hair is significantly more likely to be a Republican; rather, it simply means that if you have blonde hair, you are significantly more likely to feel it noteworthy to mention it when describing yourself if you are a Republican than if you are a Democrat.

Predicting Politics with Text Analytics

Predicting Politics with Text Analytics

Self-Image: Significant Differences

OdinText’s analysis turned up several predictors predictors for party affiliation, here are 15 examples indexed below.

  • Republicans were far more likely to include their marital status, religion, ethnicity and education level in describing themselves, and to mention that they are charitable/generous.

  • Democrats, on the other hand, were significantly more likely to describe themselves in terms of friendships, work ethic and the quality of their smile.

Interestingly, we turned up quite a few more predictors for Republicans than Democrats, suggesting that the former may be more homogeneous in terms of which aspects of their identities matter most. This translates to a somewhat higher level of confidence in predicting affinity with the Republican Party.

As an example, if you describe yourself as both “Christian” and “married,” without knowing anything else about you I can assume with 90% accuracy that you vote Republican.

Again, this does not mean that Christians who are married are more than 90% likely to be Republicans, but it does mean that people who mention these two things when asked to tell a stranger about themselves are extremely likely to be Republicans.

So What?

While this exercise was exploratory and the results should not be taken as such, it demonstrates that text analytics make it entirely possible to read between the lines and determine far more about you than one would think possible.

Obviously, there is a simpler, more direct way to determine a respondent’s political affiliation: just ask them. We did. That’s how we were able to run this analysis. But it’s hardly the point.

The point is we don’t necessarily have to ask.

In fact, we’ve already built predictive models around social media profiles and Twitter feeds that eliminate the need to pose questions—demographic, or more importantly, psychographic.

Could a political campaign put this capability to work segmenting likely voters and targeting messages? Absolutely.

But the application obviously extends well beyond politics. With an exponentially-increasing flood of Customer Experience FeedbackCRM and consumer-generated text online, marketers could predicatively model all manner of behavior with important business implications.

One final thought relating to politics: What about Donald Trump, whose supporters it has been widely noted do not all fit neatly into the conventional Republican profile? It would be pretty easy to build a predictive model for them, too! And that could be useful given the widespread reports that a significant number of people who plan to vote for him are reluctant to say so.

Preventing Customer Churn with Text Analytics

3 Ways You Can Improve Your Lost Customer Analysis

Preventing Customer Churn with Text Analytics

Lapsed Customers, Customer Churn, Customer Attrition, Customer Defection, Lost Customers, Non-Renewals, whatever you call them this kind of customer research is becoming more relevant everywhere, and we are seeing more and more companies turning to text analytics in order to better answer how to retain more customers longer.  Why are they turning to text analytics? Because no structured survey data does a better job predicting customer behavior as well as actual voice of customer text comments!

Today’s post will highlight 3 mistakes we often see being made in this kind of research.

1. Most Customer Loss/Churn Analysis is done on the customers who leave, in isolation from customers who stay. Understandable since it would make little sense to ask a customer who is still with you a survey question such as “Why have you stopped buying from us?”. But customer churn analysis can be so much more powerful if you are able to compare customers who are still with you to those who have left. There are a couple of ways to do this:

  • Whether or not you conduct a separate lapsed customer survey among those who are no longer purchasing, also consider doing a separate post-hoc analysis of your customer satisfaction survey data. It doesn’t have to be current. Just take a time period of say the last 6-9 months and analyze the comment data from those customers who have left VS those who are still with you. What did the two groups say differently just before the lapsed customers left? Can these results be used to predict who is likely to churn ahead of time? The answer is very likely yes, and in many cases you can do something about it!
  • Whenever possible text questions should be asked of all customers, not just a subgroup such as the leavers. Here sampling as well as how you ask the questions both come into play.

Consider expanding your sampling frame to include not just customers who are no longer purchasing from you, but also customers who are still purchasing from you (especially those who are purchasing more) as well as those still purchasing, but purchasing less. What you really want to understand after all is what is driving purchasing – who gives a damn if they claim they are more or less likely to recommend you – promoter and detractor analysis is over hyped!

Reducing Customer Churn

You may also consider casting an even wider sampling net than just past and current customers. Why not use a panel sample provider and try to include some competitor’s customer as well? You will need to draw the line somewhere for scope and budget, but you get the idea. The survey should be short and concise and should have the text questions up front, starting very broad (top of mind unaided) and then probe.

Begin with a question such as “Q. How, if at all, has your purchasing of Category X changed over the last couple of months?” and/or “Q. You indicated your purchasing of category X has changed, why? (Please be as specific as possible)”. Or perhaps even better, “Q. How if at all has your purchasing of category X changed over the past couple of months? If it has not changed please also explain why it hasn’t changed? (please be as specific as possible)”. As you can see, almost anyone can answer these questions no matter how much or little they have purchased. This is exactly what is needed for predictive text analytics! Having only leaver’s data will be insufficient!

2. Include other structured (real behavior data in the analysis). Some researchers analyze their survey data in isolation. Mixed data usually adds predictive power, especially if it’s real behavior data from your CRM database, and not just stated/recall behavior from your survey. In either case, the key to unlocking meaning and predictability is likely to come from the unstructured comment data. Nothing else can do a better job explaining what happened to them.

3. PLEASE PLEASE, Resist the urge to start your leaver survey with a structured question asking a battery of “check all that apply” reasons for leaving/shopping less. Your various pre-defined reasons, even if you include an “Other Specify_____” will have several negative effects on your data quality.

First, the customer will often forget their primary reason for their change in purchase frequency, they will assume incorrectly that you are most interested in these reasons you have pre-identified. Second there will be no way for you to tell which of these several reasons they are now likely to check, is truly the most important to them. Third, some customers will repeat themselves in the other specify, while others will decide not to answer it at all since they checked so many of your boxes. Either way, you’ve just destroyed the best chance you had in accurately understanding why your customers purchasing has changed!

These are many other ways to improve your insights in lapsed customer survey research by asking fewer yet better comment questions in the right order.  I hope the above tips have given you some things to consider. We’re happy to give you additional tips if you like, and we often find that as customers begin using OdinText their use of survey data both structured and unstructured improves greatly along with their understanding of their customers.

@TomHCanderson

Beyond Sentiment - What Are Emotions, and Why Are They Useful to Analyze?
Text Analytics Tips - Branding

Text Analytics Tips - Branding

Beyond Sentiment - What are emotions and why are they useful to analyze?Text Analytics Tips by Gosia

Emotions - Revealing What Really Matters

Emotions are short-term intensive and subjective feelings directed at something or someone (e.g., fear, joy, sadness). They are different from moods, which last longer, but can be based on the same general feelings of fear, joy, or sadness.

3 Components of Emotion: Emotions result from arousal of the nervous system and consist of three components: subjective feeling (e.g., being scared), physiological response (e.g., a pounding heart), and behavioral response (e.g., screaming). Understanding human emotions is key in any area of research because emotions are one of the primary causes of behavior.

Moreover, emotions tend to reveal what really matters to people. Therefore, tracking primary emotions conveyed in text can have powerful marketing implications.

The Emotion Wheel - 8 Primary Emotions

OdinText can analyze any psychological content of text but the primary attention has been paid to the power of emotions conveyed in text.

8 Primary Emotions: OdinText tracks the following eight primary emotions: joy, trust, fear, surprise, sadness, disgust, anger, and anticipation (see attached figure; primary emotions in bold).

Sentiment Analysis

Sentiment Analysis

Bipolar Nature: These primary emotions have a bipolar nature; joy is opposed to sadness, trust to disgust, fear to anger, and surprise to anticipation. Emotions in the blank spaces are mixtures of the two neighboring primary emotions.

Intensity: The color intensity dimension suggests that each primary emotion can vary in ntensity with darker hues representing a stronger emotion (e.g., terror > fear) and lighter hues representing a weaker emotion (e.g. apprehension < fear). The analogy between theory of emotions and the theory of color has been adopted from the seminal work of Robert Plutchik in 1980s. [All 32 emotions presented in the figure above are a basis for OdinText Emotional Sentiment tracking metric].

Stay tuned for more tips giving details on each of the above emotions.

Gosia

Text Analytics Tips with Gosi

Text Analytics Tips with Gosi

[NOTE: Gosia is a Data Scientist at OdinText Inc. Experienced in text mining and predictive analytics, she is a Ph.D. with extensive research experience in mass media’s influence on cognition, emotions, and behavior.